Annotate, Train, and Deploy Vision AI

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Ultralytics creates cutting-edge, state-of-the-art (SOTA) YOLO models built on years of foundational research in computer vision and AI. Constantly updated for performance and flexibility, our models are fast, accurate, and easy to use. They excel at object detection, instance segmentation, semantic segmentation, depth estimation, image classification, pose estimation, and oriented object detection tasks, and can track detected objects across video frames.

Find detailed documentation in the Ultralytics Docs. Get support via GitHub Issues. Join discussions on Discord, Reddit, and the Ultralytics Community Forums!

Request an Enterprise License for commercial use at Ultralytics Licensing.

📄 Documentation

See below for quickstart installation and usage examples. For comprehensive guidance on training, validation, prediction, and deployment, refer to our full Ultralytics Docs.

Install the ultralytics package, including all requirements, in a Python>=3.8 environment with PyTorch>=1.8.

pip install ultralytics

For alternative installation methods, including Conda, Docker, and building from source via Git, please consult the Quickstart Guide.

CLI

You can use Ultralytics YOLO directly from the Command Line Interface (CLI) with the yolo command:

# Predict using a pretrained YOLO model (e.g., YOLO26n) on an image
yolo predict model=yolo26n.pt source='https://ultralytics.com/images/bus.jpg'

The yolo command supports various tasks and modes, accepting additional arguments like imgsz=640. Explore the YOLO CLI Docs for more examples.

Python

Ultralytics YOLO can also be integrated directly into your Python projects. It accepts the same configuration arguments as the CLI:

from ultralytics import YOLO

# Load a pretrained YOLO26n model
model = YOLO("yolo26n.pt")

# Train the model on the COCO8 dataset for 100 epochs
train_results = model.train(
    data="coco8.yaml",  # Path to dataset configuration file
    epochs=100,  # Number of training epochs
    imgsz=640,  # Image size for training
    device="cpu",  # Device to run on (e.g., 'cpu', 0, [0,1,2,3])
)

# Evaluate the model's performance on the validation set
metrics = model.val()

# Perform object detection on an image
results = model("path/to/image.jpg")  # Predict on an image
results[0].show()  # Display results

# Export the model to ONNX format for deployment
path = model.export(format="onnx")  # Returns the path to the exported model

Discover more examples in the YOLO Python Docs.

✨ Models

YOLO27 — Coming Soon: Preview the upcoming family, currently undergoing final R&D. Models are not yet available, and no launch date is set.

This is the beginning of the README. Read the rest at the original. Read the full README ↗